How to use AI and OCR software to cut down on loan processing times

Applying for and finally signing a mortgage is one of the most complicated processes most homeowners will ever go through. It’s not exactly a walk in the park for lenders, either. 

Successfully processing each application involves extracting hundreds of data points, as well as verifying and analyzing all of it to assess financial risk. With the proliferation of online lenders like Rocket Loans, consumers expect near instantaneous approvals for their applications. But according to research by Ellie Mae, it usually takes legacy lenders an average of 52 days to manually process a mortgage, from the date the application is submitted to the date the loan is disbursed. 

Fortunately, automation technology is helping lenders cut their processing times down. Leading the way is optical character recognition (OCR), which can read any type of document. When augmented with artificial intelligence (AI), it can extract the information into usable electronic data, and learn to process it far quicker and with more accuracy than humans. 

OCR software is often used in conjunction with a number of other automation solutions, such as robotic process automation (RPA), which automates repetitive actions humans perform on computers. When combined with AI, this enables intelligent document processing (IDP) capabilities, which mimic the ways humans understand information and make decisions. Here’s how legacy lenders are implementing automation technology into every phase of their mortgage approval process: 

Leveraging OCR software during pre-approval

Whether mortgage applications are submitted through an online form, as a PDF, or even faxed or mailed as paper documents, OCR software can read the text, and AI can extract it as useful data ready for processing. When AI is in the mix, it can take this even further and correctly label and categorize all of the data taken from an application so initial human review and data entry isn’t necessary. 

OCR can also pull data from any income verification documents and credit reports. Then, AI can analyze the data to determine if the application should be approved or denied based on organizational rules. These systems can automatically generate approval or denial emails and send them to applicants. 

Appraisal

Once pre-approved, IDP systems can automatically route documents to appraisers and generate notifications that there are new documents to review. They can leverage conditional logic to analyze all data according to their standards, and use OCR to extract data from any additional documents they collect from the party selling the home.  

OCR improves underwriting

Underwriting is the most complex part of the mortgage approval process, but even it can benefit from automation. Users can leverage AI to verify information from central data systems rather than manually reviewing physical scans, drastically speeding up processing times. This also streamlines project management by clearly outlining to human workers which document elements may have been incorrectly or fraudulently submitted and need additional manual review. If underwriters need any additional documents, they can automate workflows that draft and send request emails to potential borrowers. 

Closing

Once underwriting is complete, the hardest work is over. However, automation software can execute workflows to distribute closing notices and gather final signatures. When users leverage the capabilities of artificial intelligence, the software can even analyze workflows and look for more opportunities to streamline processes and make them faster.

Digital mortgages are the future, and it will soon be a competitive requirement for lenders to streamline their application processes and deploy automation to speed up approval times. Automation Hero is a new end-to-end intelligent process automation system powered by deep learning AI that streamlines the entire document lifecycle. With nearly limitless integrations, Automation Hero could even expand to automate an entire enterprise.